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Neural network-based method and system for predicting shapes of three-dimensional hearths of aluminum cells in real time

An aluminum electrolytic cell and neural network technology, which is applied in the field of real-time prediction of the three-dimensional furnace shape of the aluminum electrolytic cell based on the neural network, can solve the problem of the undisclosed specific method of calculation and analysis of the shape of the aluminum electrolytic cell furnace side, the verification of the calculation results, and the inability to determine the furnace Whether the shape of the inner furnace is regular or not, to achieve the effect of ensuring convergence, improving prediction accuracy, and ensuring safe production

Active Publication Date: 2017-05-24
CENT SOUTH UNIV
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  • Application Information

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Problems solved by technology

This patent application can continuously monitor the temperature of the electrolytic cell, but neither discloses the specific method for calculating and analyzing the shape of the side of the aluminum electrolytic cell, nor does it verify the calculation results. It is impossible to determine whether the shape of the furnace in the cell is regular and whether there is a risk of leakage

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  • Neural network-based method and system for predicting shapes of three-dimensional hearths of aluminum cells in real time
  • Neural network-based method and system for predicting shapes of three-dimensional hearths of aluminum cells in real time
  • Neural network-based method and system for predicting shapes of three-dimensional hearths of aluminum cells in real time

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Embodiment 1

[0064] A neural network-based real-time prediction method for the three-dimensional furnace shape of an aluminum electrolytic cell, characterized in that it includes the following steps:

[0065] 1) Obtain the modeling parameters of a 420kA aluminum electrolytic cell, including the property data and structural parameters of each lining material, external heat exchange conditions, and process parameters, as shown in Table 1;

[0066] Table 1 Key parameters of a 420kA electrolyzer

[0067]

[0068] 2) Construct the heat transfer finite element model of the aluminum electrolytic cell according to the modeling parameters obtained in step 1. At the same time, in order to cover most of the situations in production and facilitate subsequent training, it is necessary to calculate the furnace shape under different test conditions. Therefore, different test parameters are arranged and combined to form a test group; each group of test parameters in the test group is input into the he...

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Abstract

The invention discloses a neural network-based method and system for predicting the shapes of three-dimensional hearths an aluminum cells in real time. The method comprises the following steps of: 1) obtaining aluminum cell modeling parameters; 2) constructing an aluminum cell heat transfer finite element model according to the aluminum cell modeling parameters, carrying out permutation and combination on different experimental parameters to form experimental groups, and respectively inputting each group of experimental parameters in the experimental groups into the aluminum cell heat transfer finite element model to carry out simulation calculation, so as to obtain a hearth shape and a cell shell temperature corresponding to each group experimental parameters; 3) constructing a BP neural network by taking the cell shell temperature, an electrolyte level and an aluminum level as input variables and taking the hearth shape as an output variable; 4) training the BP neural network on the basis of a simulation result in the step 2); and 5) measuring and obtaining the cell shell temperature, the electrolyte level and the aluminum level in real time, and inputting the cell shell temperature, the electrolyte level and the aluminum level into the BP neural network trained in the step 4), so as to obtain a finally predicted hearth shape. According to the method and system disclosed by the invention, the shapes of the three-dimensional hearths of the aluminum cells can be correctly and rapidly predicted.

Description

technical field [0001] The invention belongs to the field of aluminum electrolytic cells, in particular to a neural network-based real-time prediction method and system for a three-dimensional furnace shape of an aluminum electrolytic cell. Background technique [0002] In the aluminum electrolytic cell, the current passes through the molten electrolyte with a certain resistance to generate Joule heat to maintain the high temperature operation of the electrolytic cell. The molten electrolyte cools and solidifies when it contacts the side wall, forming a ring of furnace sides and outstretched legs, which can protect the side wall from being eroded by the high-temperature melt. Maintaining a suitable furnace shape can reduce the horizontal current in the molten aluminum, improve the stability of the magnetic fluid in the tank, and facilitate the stable operation of the production process. Unreasonable hearth shape will lead to deterioration of stability in the tank, lower cur...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/23
Inventor 张红亮梁金鼎李劼冉岭李天爽孙珂娜肖劲
Owner CENT SOUTH UNIV
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